The aim of this paper is to consider a method of machine learning to
analyze the problem of the sustainability of the urban transport in Italian cities.
First of all we recall the definition of sustainable mobility then we present some
indicators considered in our analysis.
The methodology used in this paper are decisional trees.
We both consider classification and regression trees. We have chosen two
different dependent variables one for classification trees (a categorical variable:
Macroregion according to NUTS 1: North West, North East, Centre, Islands and
South of Italy) and one for regression trees (a quantitative variable: PM10 maximum
number of days in excess of the human health protection limit foreseen
for PM10). In order to test the performance of this methodology we have applied
random forest.
The analysis has been performed using SAS language.
